




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def        __UpperCAmelCase						(							)       ->  Tuple:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      Image.open(requests.get(lowerCamelCase_  ,							stream=lowerCamelCase_  ).raw  ).convert('RGB'  )

			return image





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      []
			# fmt: off

			# vision encoder
			rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')  )
			rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')  )
			rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')  )
			rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')  )
			rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')  )
			rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')  )

			for i in range(config.vision_config.num_hidden_layers  ):
						rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',)  )
						rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight')  )
						rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias')  )

			# QFormer
			rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')  )
			rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')  )

			# fmt: on
			return rename_keys





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Dict  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      dct.pop(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      val





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[Any]:







			"""simple docstring"""





			for i in range(config.vision_config.num_hidden_layers  ):
						# read in original q and v biases
						SCREAMING_SNAKE_CASE_   : str					  =      state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'  )
						SCREAMING_SNAKE_CASE_   : Dict					  =      state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'  )

						# next, set bias in the state dict
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.cat((q_bias, torch.zeros_like(lowerCamelCase_  ,							requires_grad=lowerCamelCase_  ), v_bias)  )
						SCREAMING_SNAKE_CASE_   : List[str]					  =      qkv_bias





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      3_64 if 'coco' in model_name else 2_24
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      InstructBlipVisionConfig(image_size=lowerCamelCase_  ).to_dict()

			# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
			# seems like flan-T5 models don't have bos_token_id properly set?
			if "t5-xl" in model_name:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TaConfig.from_pretrained('google/flan-t5-xl'  ,							dense_act_fn='gelu'  ,							bos_token_id=1  ).to_dict()
			elif "t5-xxl" in model_name:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      TaConfig.from_pretrained('google/flan-t5-xxl'  ,							dense_act_fn='gelu'  ,							bos_token_id=1  ).to_dict()
			elif "vicuna-7b" in model_name:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'  ,							vocab_size=3_20_01  ).to_dict()
			elif "vicuna-13b" in model_name:
						SCREAMING_SNAKE_CASE_   : Dict					  =      LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'  ,							vocab_size=3_20_01  ).to_dict()
			else:
						raise ValueError('Model name not supported'  )

			# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      InstructBlipQFormerConfig(vocab_size=3_05_23  ).to_dict()
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      InstructBlipConfig(vision_config=lowerCamelCase_  ,							text_config=lowerCamelCase_  ,							qformer_config=lowerCamelCase_  )

			return config, image_size





@torch.no_grad()
def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Any=None  ,							lowerCamelCase_ :     Any=False  )       ->  Union[str, Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      AutoTokenizer.from_pretrained('bert-base-uncased'  ,							truncation_side='left'  )
			qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}  )

			if "t5" in model_name:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      TaTokenizerFast.from_pretrained('google/flan-t5-xl'  ,							truncation_side='left'  )
			elif "vicuna" in model_name:
						# the following was used in the original implementation:
						# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
						# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
						# tokenizer.add_special_tokens({"bos_token": "</s>"})
						# tokenizer.add_special_tokens({"eos_token": "</s>"})
						# tokenizer.add_special_tokens({"unk_token": "</s>"})
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      LlamaTokenizerFast.from_pretrained(
						    'huggyllama/llama-7b'  ,							truncation_side='left'  ,							bos_token='</s>'  ,							unk_token='</s>'  )
						tokenizer.add_special_tokens({'pad_token': '[PAD]'}  )

			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      get_blipa_config(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      InstructBlipForConditionalGeneration(lowerCamelCase_  ).eval()

			SCREAMING_SNAKE_CASE_   : str					  =      {
			    'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
			    'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
			    'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
			    'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
			}

			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model_name_to_original[model_name]

			# load original model
			print('Loading original model...'  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      'cuda:1' if torch.cuda.is_available() else 'cpu'
			SCREAMING_SNAKE_CASE_   : Any					  =      'cuda:2' if torch.cuda.is_available() else 'cpu'
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      load_model_and_preprocess(
			    name=lowerCamelCase_  ,							model_type=lowerCamelCase_  ,							is_eval=lowerCamelCase_  ,							device=lowerCamelCase_  )
			original_model.eval()
			print('Done!'  )

			# update state dict keys
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      original_model.state_dict()
			SCREAMING_SNAKE_CASE_   : Dict					  =      create_rename_keys(lowerCamelCase_  )
			for src, dest in rename_keys:
						rename_key(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      state_dict.pop(lowerCamelCase_  )
						if key.startswith('Qformer.bert'  ):
									SCREAMING_SNAKE_CASE_   : Tuple					  =      key.replace('Qformer.bert'  ,							'qformer'  )
						if "attention.self" in key:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      key.replace('self'  ,							'attention'  )
						if "llm_proj" in key:
									SCREAMING_SNAKE_CASE_   : Any					  =      key.replace('llm_proj'  ,							'language_projection'  )
						if "t5_proj" in key:
									SCREAMING_SNAKE_CASE_   : str					  =      key.replace('t5_proj'  ,							'language_projection'  )
						if key.startswith('llm_model'  ):
									SCREAMING_SNAKE_CASE_   : Any					  =      key.replace('llm_model'  ,							'language_model'  )
						if key.startswith('t5'  ):
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      key.replace('t5'  ,							'language'  )
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      val

			# read in qv biases
			read_in_q_v_bias(lowerCamelCase_  ,							lowerCamelCase_  )

			# note: weights get loaded in torch.float32 by default
			hf_model.load_state_dict(lowerCamelCase_  ,							strict=lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : str					  =      load_demo_image()
			SCREAMING_SNAKE_CASE_   : Any					  =      'What is unusual about this image?'

			# create processor
			SCREAMING_SNAKE_CASE_   : Dict					  =      BlipImageProcessor(
			    size={'height': image_size, 'width': image_size}  ,							image_mean=lowerCamelCase_  ,							image_std=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      InstructBlipProcessor(
			    image_processor=lowerCamelCase_  ,							tokenizer=lowerCamelCase_  ,							qformer_tokenizer=lowerCamelCase_  ,							)
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      processor(images=lowerCamelCase_  ,							text=lowerCamelCase_  ,							return_tensors='pt'  ).to(lowerCamelCase_  )

			# make sure processor creates exact same pixel values
			SCREAMING_SNAKE_CASE_   : str					  =      vis_processors['eval'](lowerCamelCase_  ).unsqueeze(0  ).to(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      inputs.pixel_values
			assert torch.allclose(original_pixel_values.to(pixel_values.device  )  ,							lowerCamelCase_  )

			original_model.to(lowerCamelCase_  )
			hf_model.to(lowerCamelCase_  )
			with torch.no_grad():
						if "vicuna" in model_name:
									SCREAMING_SNAKE_CASE_   : str					  =      original_model({'image': original_pixel_values, 'text_input': [prompt]}  ).logits
									SCREAMING_SNAKE_CASE_   : Tuple					  =      hf_model(**lowerCamelCase_  ).logits
						else:
									SCREAMING_SNAKE_CASE_   : Any					  =      original_model(
									    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}  ).logits
									SCREAMING_SNAKE_CASE_   : str					  =      tokenizer('\n'  ,							return_tensors='pt'  ).input_ids.to(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : str					  =      label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id  ,							-1_00  )
									SCREAMING_SNAKE_CASE_   : Dict					  =      hf_model(**lowerCamelCase_  ,							labels=lowerCamelCase_  ).logits

			print('First values of original logits:'  ,							original_logits[0, :3, :3]  )
			print('First values of HF logits:'  ,							logits[0, :3, :3]  )

			# assert values
			assert original_logits.shape == logits.shape
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      1E-4 if 'vicuna' in model_name else 1E-5
			assert torch.allclose(original_logits.to(logits.device  )  ,							lowerCamelCase_  ,							atol=lowerCamelCase_  )
			print('Looks ok!'  )

			print('Generating with original model...'  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      original_model.generate({'image': original_pixel_values, 'prompt': prompt}  ,							num_beams=5  )

			# important: we need to cast the weights of the HF model to the appropriate type
			print('Generating with HF model...'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      hf_model.generate(
			    **lowerCamelCase_  ,							do_sample=lowerCamelCase_  ,							num_beams=5  ,							max_length=2_56  ,							min_length=1  ,							top_p=0.9  ,							repetition_penalty=1.5  ,							length_penalty=1.0  ,							temperature=1  ,							)
			if "vicuna" in model_name:
						# convert output id 0 to 2 (eos_token_id)
						# TODO add this in the generate method?
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      2
			print('Original generation:'  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      processor.batch_decode(lowerCamelCase_  ,							skip_special_tokens=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [text.strip() for text in output_text]
			print('HF generation:'  ,							lowerCamelCase_  )

			if pytorch_dump_folder_path is not None:
						processor.save_pretrained(lowerCamelCase_  )
						hf_model.save_pretrained(lowerCamelCase_  )

			if push_to_hub:
						processor.push_to_hub(F'Salesforce/{model_name}'  )
						hf_model.push_to_hub(F'Salesforce/{model_name}'  )


if __name__ == "__main__":
						UpperCamelCase__ :   int						=							argparse.ArgumentParser()
						UpperCamelCase__ :   int						=							[
						    '''instructblip-vicuna-7b''',
						    '''instructblip-vicuna-13b''',
						    '''instructblip-flan-t5-xl''',
						    '''instructblip-flan-t5-xxl''',
						]
						parser.add_argument(
						    '''--model_name''',
						    default='''instructblip-flan-t5-xl''',
						    choices=choices,
						    type=str,
						    help='''Path to hf config.json of model to convert''',
						)
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--push_to_hub''',
						    action='''store_true''',
						    help='''Whether to push the model and processor to the hub after converting''',
						)

						UpperCamelCase__ :   Optional[Any]						=							parser.parse_args()

						convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
    import bsa
    from bsa import BeautifulSoup


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Union[str, Any]     ,				**UpperCamelCase__     :					Union[str, Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    requires_backends(self     ,				["""bs4"""]       )
    super().__init__(**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			element if element.name else element.parent
    for parent in child.parents:  # type: bs4.element.Tag
      __magic_name__								=			parent.find_all(child.name     ,				recursive=UpperCamelCase__       )
      xpath_tags.append(child.name       )
      xpath_subscripts.append(
          0 if 1 == len(UpperCamelCase__       ) else next(i for i, s in enumerate(UpperCamelCase__     ,				1       ) if s is child       )       )
      __magic_name__								=			parent
    xpath_tags.reverse()
    xpath_subscripts.reverse()
    return xpath_tags, xpath_subscripts
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			BeautifulSoup(UpperCamelCase__     ,				"""html.parser"""       )

    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			[]

    for element in html_code.descendants:
      if type(UpperCamelCase__       ) == bsa.element.NavigableString:
        if type(element.parent       ) != bsa.element.Tag:
          continue

        __magic_name__								=			html.unescape(UpperCamelCase__       ).strip()
        if not text_in_this_tag:
          continue

        all_doc_strings.append(UpperCamelCase__       )

        __magic_name__	,       __magic_name__								=			self.xpath_soup(UpperCamelCase__       )
        stringaxtag_seq.append(UpperCamelCase__       )
        stringaxsubs_seq.append(UpperCamelCase__       )

    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xtags does not correspond"""       )
    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xsubs does not correspond"""       )

    return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			""""""
    for tagname, subs in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      xpath += F'''/{tagname}'''
      if subs != 0:
        xpath += F'''[{subs}]'''
    return xpath





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple       )     ->      BatchFeature:







    """simple docstring"""






    __magic_name__								=			False

    # Check that strings has a valid type
    if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
      __magic_name__								=			True
    elif isinstance(UpperCamelCase__     ,				(list, tuple)       ):
      if len(UpperCamelCase__       ) == 0 or isinstance(html_strings[0]     ,				UpperCamelCase__       ):
        __magic_name__								=			True

    if not valid_strings:
      raise ValueError(
          """HTML strings must of type `str`, `List[str]` (batch of examples), """
          F'''but is of type {type(UpperCamelCase__       )}.'''       )

    __magic_name__								=			bool(isinstance(UpperCamelCase__     ,				(list, tuple)       ) and (isinstance(html_strings[0]     ,				UpperCamelCase__       ))       )

    if not is_batched:
      __magic_name__								=			[html_strings]

    # Get nodes + xpaths
    __magic_name__								=			[]
    __magic_name__								=			[]
    for html_string in html_strings:
      __magic_name__	,       __magic_name__	,       __magic_name__								=			self.get_three_from_single(UpperCamelCase__       )
      nodes.append(UpperCamelCase__       )
      __magic_name__								=			[]
      for node, tag_list, sub_list in zip(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			self.construct_xpath(UpperCamelCase__     ,				UpperCamelCase__       )
        xpath_strings.append(UpperCamelCase__       )
      xpaths.append(UpperCamelCase__       )

    # return as Dict
    __magic_name__								=			{"""nodes""": nodes, """xpaths""": xpaths}
    __magic_name__								=			BatchFeature(data=UpperCamelCase__     ,				tensor_type=UpperCamelCase__       )

    return encoded_inputs

